Title :
Asymptotic statistical properties of AR spectral estimators for processes with mixed spectra
Author :
Lau, Soon-Sen ; Sherman, Peter J. ; White, Langford B.
Author_Institution :
Qualcomm Inc., San Diego, CA, USA
fDate :
4/1/2002 12:00:00 AM
Abstract :
The influence of a point spectrum on large sample statistics of the autoregressive (AR) spectral estimator is addressed. In particular, the asymptotic distributions of the AR coefficients, the innovations variance, and the spectral density estimator of a finite-order AR(p) model to a mixed spectrum process are presented. Various asymptotic results regarding AR modeling of a regular process with a continuous spectrum are arrived at as special cases of the results for the mixed spectrum setting. Finally, numerical simulations are performed to verify the analytical results
Keywords :
autoregressive processes; parameter estimation; spectral analysis; statistical analysis; AR coefficients; AR modeling; AR spectral estimators; asymptotic statistical properties; autoregressive spectral estimator; continuous spectrum; finite-order model; innovations variance; large sample statistics; mixed spectra processes; mixed spectrum process; numerical simulations; spectral density estimator; Colored noise; Least squares approximation; Linear regression; Numerical simulation; Performance analysis; Predictive models; Spectral analysis; Statistical distributions; Stochastic processes; Technological innovation;
Journal_Title :
Information Theory, IEEE Transactions on
